RODEO：通过暴露自适应分布外样本实现鲁棒的离群点检测
RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples
AI总结：
本文提出RODEO方法，结合离群点暴露与对抗训练，利用文本到图像模型生成多样且近分布的离群样本，显著提升了图像离群点检测在对抗环境下的鲁棒性。
摘要：
近年来，各种形式的图像离群点检测取得了显著进展。然而，对抗环境下的离群点检测性能远远落后于标准环境下的表现。这是由于在训练期间缺乏对对抗场景的有效暴露，尤其是在未见过的离群点上，导致检测模型无法学习到鲁棒特征。为了弥合这一差距，我们引入了RODEO，这是一种以数据为中心的方法，用于生成有效的离群点以实现鲁棒的离群点检测。更具体地说，我们证明，只要暴露的训练离群点满足某些特征，包括多样性，以及与内点样本在概念上的可区分性和类比性，将离群点暴露（OE）和对抗训练结合起来可以成为实现这一目标的有效策略。我们利用文本到图像模型来实现这一目标。我们定量和定性地证明，我们的自适应OE方法利用来自文本和图像领域的信息，有效地生成了“多样化”和“近分布”的离群点。此外，我们的实验结果表明，利用我们合成的离群点显著增强了离群点检测器的性能，特别是在对抗环境中。
英文摘要：
In recent years, there have been significant improvements in various forms of image outlier detection. However, outlier detection performance under adversarial settings lags far behind that in standard settings. This is due to the lack of effective exposure to adversarial scenarios during training, especially on unseen outliers, leading to detection models failing to learn robust features. To bridge this gap, we introduce RODEO, a data-centric approach that generates effective outliers for robust outlier detection. More specifically, we show that incorporating outlier exposure (OE) and adversarial training can be an effective strategy for this purpose, as long as the exposed training outliers meet certain characteristics, including diversity, and both conceptual differentiability and analogy to the inlier samples. We leverage a text-to-image model to achieve this goal. We demonstrate both quantitatively and qualitatively that our adaptive OE method effectively generates ``diverse'' and ``near-distribution'' outliers, leveraging information from both text and image domains. Moreover, our experimental results show that utilizing our synthesized outliers significantly enhances the performance of the outlier detector, particularly in adversarial settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
